plotly¶

This provides an overview to using plotly as the plotting engine with fivecentplots

Setup¶

Import packages

%load_ext autoreload
%autoreload 2
import fivecentplots as fcp
import pandas as pd
import imageio.v3 as imageio
import cv2
from pathlib import Path
fcp.__version__

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

'0.6.0-a2'

Sample data¶

Several data sets are used in this tutorial:

x-y data¶

df_xy = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data.csv')
df_xy.head(10)

	Substrate	Target Wavelength	Boost Level	Temperature [C]	Die	Voltage
0	Si	450	0.2	25	(1,1)	0.0
1	Si	450	0.2	25	(1,1)	0.1
2	Si	450	0.2	25	(1,1)	0.2
3	Si	450	0.2	25	(1,1)	0.3
4	Si	450	0.2	25	(1,1)	0.4
5	Si	450	0.2	25	(1,1)	0.5
6	Si	450	0.2	25	(1,1)	0.6
7	Si	450	0.2	25	(1,1)	0.7
8	Si	450	0.2	25	(1,1)	0.8
9	Si	450	0.2	25	(1,1)	0.9

Time series data¶

ts = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_ts.csv')
ts.head()

	Date	Happiness Quotient
0	1/1/2015	16.088954
1	1/2/2015	18.186724
2	1/3/2015	35.744313
3	1/4/2015	38.134045
4	1/5/2015	46.147279

Bar plot data¶

df_bar = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_bar.csv')
df_bar.head()

	Liquid	pH	Measurement	T [C]
0	Lemon juice	2.4	A	25
1	Orange juice	3.5	A	25
2	Battery acid	1.0	A	25
3	Bottled water	6.7	A	25
4	Coke	3.0	A	25

Box plot data¶

df_box = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_box.csv')
df_box.head()

	Batch	Sample	Region	Value	ID
0	101	1	Alpha123	3.5	ID701223A
1	101	1	Alpha123	0.0	ID7700-1222B
2	101	1	Alpha123	3.3	ID701223A
3	101	1	Alpha123	3.2	ID7700-1222B
4	101	1	Alpha123	4.0	ID701223A

Contour data¶

df_contour = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_contour.csv')
df_contour.head()

	Experiment	Batch	X	Y	Value
0	Control	101	1	-4	3.5
1	Control	101	1	-2	2.1
2	Control	101	1	0	3.3
3	Control	101	1	2	3.2
4	Control	101	1	4	4.0

Gantt data¶

df_gantt = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_gantt.csv')
df_gantt.head()

	Task	Assigned	Start	Stop	Category
0	Record drums	Taylor	2010-09-01	2010-09-05	Recording
1	Record bass	Nate	2010-09-04	2010-09-06	Recording
2	Record rhythm guitar	Pat	2010-09-06	2010-09-08	Recording
3	Record rhythm guitar	Dave	2010-09-06	2010-09-08	Recording
4	Record lead guitar	Chris	2010-09-07	2010-09-09	Recording

Heatmap data¶

df_heatmap = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_heatmap.csv')
df_heatmap.head()

	Player	Category	Average
0	Lebron James	Points	27.5
1	Lebron James	Assists	9.1
2	Lebron James	Rebounds	8.6
3	Lebron James	Blocks	0.9
4	James Harden	Points	30.4

Histogram data¶

df_hist = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_box.csv')
img_hist = cv2.imread(str(Path(fcp.__file__).parent / 'test_data/imshow_cat_pirate.png'))

Image data¶

img_bgr = cv2.imread(str(Path(fcp.__file__).parent / 'test_data/imshow_cat_pirate.png'))
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)

Theme and shortcuts¶

Load a custom theme file for plotly:

fcp.set_theme('gray_plotly')

Previous theme file found! Renaming to "defaults_old.py" and copying theme "gray_plotly"...done!

Note

While many theme parameters can be common between matplotlib and plotly, certain differences between the engines (such as the default dpi) make it easier to use a custom theme file for each engine

Define some scope-wide shortcuts to avoid having to enter certain kwargs into each function call (i.e., these are automatically added):

fcp.KWARGS['inline'] = True
fcp.KWARGS['engine'] = 'plotly'

Caution¶

Not all features available in matplotlib are available in plotly. fivecentplots will attempt to warn you if you are requesting an unsupported feature, but not every possible option has been tested. Buyer beware (and then file a bug report!)

`plot`¶

Scatter¶

No legend¶

fcp.plot(df_xy, x='Voltage', y='I [A]', lines=False, ax_size=[400, 400])

Filtered¶

fcp.plot(df_xy, x='Voltage', y='I [A]', title='IV Data', lines=False,
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')

Legend¶

fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die',
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')

Log axis¶

Note

symlog and logit not yet supported

fcp.plot(df_xy, x='Voltage', y='I [A]', ax_scale='loglog', legend='Die', xmin=0.9, xmax=2.1, grid_minor=True,
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')

Categorical tick labels¶

fcp.plot(df_xy, x='Die', y='I [A]',
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25 & Voltage==1.5')

Time series¶

fcp.plot(ts, x='Date', y='Happiness Quotient', markers=False, ax_size=[1000, 250])

Secondary axes¶

Note

Not all secondary and multiple axes cases are shown below but all the matplotlib examples in plot have been ported

fcp.plot(df_xy, x='Voltage', y=['Voltage', 'I [A]'], twin_x=True, legend='Die',
         grid_major_y2=True, grid_major_y2_style='--', y2max=1.4,
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25 & Die=="(-1,2)"')

Multiple x & y values¶

fcp.plot(df_xy, x=['Boost Level', 'I [A]'], y=['Voltage', 'Temperature [C]'], legend='Die',
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')

Grouping¶

Row plot¶

fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', row='Boost Level', ax_size=[225, 225], legend_edge_color='#000000',
         filter='Substrate=="Si" & Target Wavelength==450 & Temperature [C]==25', label_row_fill_color='#000000', ax_edge_width=4,
         label_row_font_color='#ff0000', label_row_edge_color='#0000ff', label_row_edge_width=2, legend_edge_width=3)

Warning: The following kwargs are not supported:
    - label_row_font_color

Column plot¶

fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', col='Boost Level', ax_size=[225, 225], legend_edge_color='#000000', ax_edge_width=5,
         filter='Substrate=="Si" & Target Wavelength==450 & Temperature [C]==25', label_col_fill_color='#000000', label_col_font_color='#ff0000')

Warning: The following kwargs are not supported:
    - label_col_font_color

Row x column grid¶

fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', col='Boost Level', row='Temperature [C]',
         modebar_visible=True, modebar_fill_color='#000000', ax_edge_width=1, ymin=0,
         ax_size=[225, 225], filter='Substrate=="Si" & Target Wavelength==450', label_rc_font_size=13)

Wrap plot¶

fcp.plot(df_xy, x='Voltage', y='I [A]', legend='Die', wrap=['Temperature [C]', 'Boost Level'],
         ax_size=[225, 225], filter='Substrate=="Si" & Target Wavelength==450', label_rc_font_size=13)

Horizontal & vertical lines¶

fcp.plot(df_xy, x='Voltage', y='I [A]', title='IV Data', lines=False, legend=True,
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25',
         ax_hlines=[(0, '#FF0000', '--', 3, 1, 'Open', '#555555', 0.25), 1.2],
         ax_vlines=[(0.6, '#0000ff', ':'), (1, '#00FF00')])

Curve fitting¶

fcp.plot(df_xy, x='Voltage', y='I [A]', title='IV Data', lines=False,
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25',
         fit=1, fit_eqn=True, fit_rsq=True, fit_range_x=[1.3, 2])

Stat Lines¶

fcp.plot(df_xy, x='Voltage', y=['Boost Level', 'I [A]'], legend=True, stat='median',
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25')

Confidence intervals¶

To demonstrate this feature we will use a special dataset:

df_interval = pd.read_csv(Path(fcp.__file__).parent / 'test_data/fake_data_interval.csv')
df_interval.head()

	x	y
0	-1.0	-10.715459
1	-1.0	-9.972410
2	-1.0	-30.740532
3	-1.0	-31.368963
4	-1.0	-29.058633

fcp.plot(df_interval, x='x', y='y', lines=False, conf_int=0.95)

Control limits¶

fcp.plot(df_interval, x='x', y='y', lines=False, ucl=50, lcl=-50, ucl_fill_color='#FF0000', legend=True)

Reference line¶

fcp.plot(df_xy, x='Voltage', y='I [A]', title='IV Data', legend='Die',
         filter='Substrate=="Si" & Target Wavelength==450 & Boost Level==0.2 & Temperature [C]==25',
         ref_line=df_xy['Voltage'], ref_line_legend_text='y=x', xmin=0, ymin=0, xmax=1.6, ymax=1.6)

`bar`¶

fcp.bar(df_bar, x='Liquid', y='pH', filter='Measurement=="A" & T [C]==25', horizontal=True)

fcp.bar(df_bar, x='Liquid', y='pH', tick_labels_major_x_rotation=90, legend='Measurement')

fcp.bar(df_bar, x='Liquid', y='pH', tick_labels_major_x_rotation=90, stacked=True, legend='Measurement')

fcp.bar(df_bar, x='Liquid', y='pH', tick_labels_major_x_rotation=90, col='Measurement', row='T [C]', ax_hlines=0, ax_size=[300, 300])

`boxplot`¶

fcp.boxplot(df_box, y='Value', groups=['Batch', 'Sample'], group_means=True)

df_box['Row'] = [int(f) for f in df_box.index / 4]
fcp.boxplot(df_box, y='Value', groups=['Batch', 'Sample'], mean_diamonds=True, conf_coeff=0.95, legend='Row')

fcp.boxplot(df_box, y='Value', groups=['Batch', 'Sample'], violin=True)

fcp.boxplot(df_box, y='Value', groups=['Batch', 'Sample'], col='Region', ax_size=[300, 300])

`contour`¶

fcp.contour(df_contour, x='X', y='Y', z='Value', filled=False, cbar=False)

fcp.contour(df_contour, x='X', y='Y', z='Value', filled=False, levels=40, contour_width=2,
            xmin=-4, xmax=5, ymin=-4, ymax=6, cbar=True,
            show_points=True, marker_size=26, marker_fill_color='#00FF00')

Warning: The following kwargs are not supported:
    - contour_width

fcp.contour(df_contour, x='X', y='Y', z='Value', row='Batch', col='Experiment', filled=True,
            cbar=False, xmin=-3, xmax=3, ymin=-3, ymax=3, ax_size=[250,250],
            label_rc_font_size=12, levels=40)

`gantt`¶

Better luck next time…

`heatmap`¶

fcp.heatmap(df_heatmap, x='Category', y='Player', z='Average')

`hist`¶

fcp.hist(df_hist, x='Value', horizontal=True, bins=20)

fcp.hist(df_hist, x='Value', legend='Region', kde=True, kde_width=2)

Warning: The following kwargs are not supported:
    - kde_width

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[48], line 1
----> 1 fcp.hist(df_hist, x='Value', legend='Region', kde=True, kde_width=2)

File ~/Code/fivecentplots/src/fivecentplots/fcp.py:502, in hist(df, **kwargs)
    409 def hist(df, **kwargs):
    410     """Histogram plot.
    411
    412     Args:
   (...)
    499             .. figure:: ../_static/images/example_hist2.png
    500     """
--> 502     return plotter(data.Histogram, **utl.dfkwarg(df, kwargs, data.Histogram))

File ~/Code/fivecentplots/src/fivecentplots/fcp.py:1686, in plotter(dobj, **kwargs)
   1683 kwargs['timer'].get(f'ifig={ifig} | ir={ir} | ic={ic} | add_hvlines')
   1685 # Plot the data
-> 1686 dd = globals()['plot_{}'.format(dd.name)](dd, layout, ir, ic, df_rc, kwargs)
   1687 kwargs['timer'].get(f'ifig={ifig} | ir={ir} | ic={ic} | plot')
   1689 # Add rc labels

File ~/Code/fivecentplots/src/fivecentplots/fcp.py:1416, in plot_hist(data, layout, ir, ic, df_rc, kwargs)
   1399 """
   1400 Plot data as histogram
   1401
   (...)
   1409
   1410 """
   1411 for iline, df, x, y, z, leg_name, twin, ngroups in data.get_plot_data(df_rc):
   1412     # if kwargs.get('groups', False):  # no use case for this
   1413     #     for nn, gg in df.groupby(utl.validate_list(kwargs['groups'])):
   1414     #         hist, data = layout.plot_hist(ir, ic, iline, gg, x, y, leg_name, data)
   1415     # else:
-> 1416     hist, data = layout.plot_hist(ir, ic, iline, df, x, y, leg_name, data)
   1418 return data

File ~/Code/fivecentplots/src/fivecentplots/engines/plotly.py:1602, in Layout.plot_hist(self, ir, ic, iline, df, x, y, leg_name, data)
   1600     y0 = np.linspace(data.ranges['ymin'][ir, ic], data.ranges['ymax'][ir, ic], 1000)
   1601     x0 = kde(y0)
-> 1602 kwargs = self.make_kw_dict(self.kde)
   1603 kwargs['color'] = RepeatedList(kwargs['color'][iline], 'color')
   1604 mls = dict(line=dict(width=self.kde.width[iline],
   1605                      color=self.kde.color[iline],
   1606                      dash=self.kde.style[iline],
   1607                      ))

File ~/Code/fivecentplots/src/fivecentplots/engines/layout.py:2776, in BaseLayout.make_kw_dict(self, element, pop)
   2774 kwargs['edge_color'] = copy.copy(element.edge_color)
   2775 kwargs['edge_width'] = copy.copy(element.edge_width)
-> 2776 kwargs['edge_width_adj'] = copy.copy(element.edge_width_adj)
   2777 kwargs['font'] = copy.copy(element.font)
   2778 kwargs['font_weight'] = copy.copy(element.font_weight)

AttributeError: 'Element' object has no attribute 'edge_width_adj'

[ ]:

fcp.hist(img_rgb, legend='Channel', markers=False, ax_size=[600, 400], line_width=2, colors=fcp.RGB)

> /Users/steve/Code/fivecentplots/src/fivecentplots/engines/plotly.py(2033)show()
   2031             # pio.renderers.default = 'iframe'  # not sure about this
   2032             db()
-> 2033             self.fig.obj.show(config=config)
   2034 
   2035     def update_markers(self):

`imshow`¶

fcp.imshow(img_rgb, ax_size=[600, 300])

img_raw = fcp.utilities.img_grayscale(img_rgb)
fcp.imshow(img_raw, ax_size=[600, 600], title='Fake RAW, Fake Pirate', cbar=True, title_edge_width=1, title_edge_color='#ff0000')

url = 'https://upload.wikimedia.org/wikipedia/commons/2/28/RGB_illumination.jpg'
img_rgb_sp = imageio.imread(url)
img_raw_sp = fcp.utilities.rgb2bayer(img_rgb_sp)
fcp.imshow(img_raw_sp, cmap='inferno', ax_size=[300, 300], cfa='rggb', wrap='Plane', ax_edge_width=1, ax_edge_color='#555555')

`nq`¶

fcp.nq(df_box, x='Value', marker_size=4, line_width=2)

img_rgb = cv2.imread(str(Path(fcp.__file__).parent / 'test_data/imshow_cat_pirate.png'))
img_cat = fcp.utl.img_grayscale(img_rgb, bit_depth=12)
fcp.nq(img_cat, marker_size=4, line_width=2)

`pie`¶

df_pie = df_bar.copy()
df_pie.loc[df_pie.pH < 0, 'pH'] = -df_pie.pH
fcp.pie(df_pie, x='Liquid', y='pH', filter='Measurement=="A" & T [C]==25')

fcp.pie(df_pie, x='Liquid', y='pH', filter='Measurement=="A" & T [C]==25', explode=[0.1], legend=True)

> /Users/steve/Code/fivecentplots/src/fivecentplots/engines/plotly.py(2458)show()
   2456             # pio.renderers.default = 'iframe'  # not sure about this
   2457             db()
-> 2458             self.fig.obj.show(config=config)
   2459 
   2460     def update_markers(self):

plotly¶

Setup¶

Sample data¶

x-y data¶

Time series data¶

Bar plot data¶

Box plot data¶

Contour data¶

Gantt data¶

Heatmap data¶

Histogram data¶

Image data¶

Theme and shortcuts¶

Caution¶

plot¶

Scatter¶

No legend¶

Filtered¶

Legend¶

Log axis¶

Categorical tick labels¶

Time series¶

Secondary axes¶

Multiple x & y values¶

Grouping¶

Row plot¶

Column plot¶

Row x column grid¶

Wrap plot¶

Horizontal & vertical lines¶

Curve fitting¶

Stat Lines¶

Confidence intervals¶

Control limits¶

Reference line¶

bar¶

boxplot¶

contour¶

gantt¶

heatmap¶

hist¶

imshow¶

nq¶

pie¶

`plot`¶

`bar`¶

`boxplot`¶

`contour`¶

`gantt`¶

`heatmap`¶

`hist`¶

`imshow`¶

`nq`¶

`pie`¶